Todd DiLullo

# Assignment 8

output: html_document: pdf_document: default word_document: default title: “Assignment 8: Visualization - Making a bar race” —

How to do it?:

• Open the Rmarkdown file of this assignment (link) in Rstudio.

• Right under each question, insert a code chunk (you can use the hotkey Ctrl + Alt + I to add a code chunk) and code the solution for the question.

• Knit the rmarkdown file (hotkey: Ctrl + Alt + K) to export an html.

• Publish the html file to your Githiub Page.

Submission: Submit the link on Github of the assignment to Canvas

---

1. Install two packages gganimate and gifski then restart Rstudio. Using the Adult Census Income data, make an animation using geom_point and transition_states.

#install.packages('gifski')
library(gganimate)
library(gifski)
library(ggplot2)
library(tidyverse)
library(knitr)
df <- read_csv('https://bryantstats.github.io/math421/data/adult_census.csv')

g <- ggplot(df, 
       aes(x = capital.loss, 
           y = hours.per.week))+
  geom_point(size=4) + 
  transition_states(income)+
labs(title = 'income: {closest_state}')
animate(g)

2. Using the Adult Census Income data, make an animation using geom_bar and transition_states.

df %>% ggplot(aes(x = income,
                  fill=sex))+
  geom_bar(position = 'fill')+
  transition_states(education) +
  labs(title = 'education: {closest_state}')

3. Use the WHO’s dataset at this link. Make a top-10 bar race by months between countries on the number of deaths by Covid 19 in 2021.

df <- read_csv('https://covid19.who.int/WHO-COVID-19-global-data.csv')

library(lubridate)
df$Country <- str_replace(df$Country, "United Kingdom of Great Britain and Northern Ireland", "UK and NI")
df$months <- month(df$Date_reported)
d1 <- df %>% group_by(months, Country) %>% summarise(mean = mean(Cumulative_deaths))
d2 <- d1 %>% group_by(months) %>% mutate(rank=rank(-mean)) 
d3 <- d2 %>% filter(rank <= 10)

a1 <- d3 %>% ggplot(aes(x=rank, y=mean, group=Country, fill=Country, label=Country)) + geom_col()+
    geom_text(aes(y = mean, label = Country), hjust = 1.4)+ 
    coord_flip(clip = "off", expand = FALSE) +scale_x_reverse()+
    labs(title = 'month {closest_state}', x='', y='Cumulative Deaths', fill='Country')+
    theme(plot.title = element_text(hjust = 1, size = 22),
          axis.ticks.y = element_blank(),
          axis.text.y  = element_blank()) + 
    transition_states(months)+
    ease_aes("cubic-in-out")
animate(a1, nframes = 400)

4. Make a bar race using a dataset of your own interest. You may use the dataset that we use in class (https://covidtracking.com/data/download/all-states-history.csv) but you should make a different bar racev from ones in the slides.

df <- read_csv('https://covidtracking.com/data/download/all-states-history.csv')

## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
##   dat <- vroom(...)
##   problems(dat)

df <- df %>% filter(deathIncrease>0, positiveIncrease>0)
library(lubridate)
df$months <- months(df$date)
d1 <- df %>% group_by(months, state) %>% summarise(mean = mean(death))
d2 <- d1 %>% group_by(months) %>% mutate(rank=rank(-mean)) 
d3 <- d2 %>% filter(rank <= 10)
a1 <- d3 %>% ggplot(aes(x=rank, y=mean, group=state, fill=state, label=state)) + geom_col()+
    geom_text(aes(y = mean, label = state), hjust = 1.4)+ 
    coord_flip(clip = "off", expand = FALSE) +scale_x_reverse()+
    labs(title = 'month {closest_state}', x='', y='Total Number of Deaths', fill='state')+
    theme(plot.title = element_text(hjust = 1, size = 22),
          axis.ticks.y = element_blank(),
          axis.text.y  = element_blank()) + 
    transition_states(months)+
    ease_aes("cubic-in-out")
animate(a1, nframes = 400)